Using Data to Identify Retention Issues

𝐀 $𝟓𝐌 𝐚𝐭𝐭𝐫𝐢𝐭𝐢𝐨𝐧 𝐩𝐫𝐨𝐛𝐥𝐞𝐦—𝐬𝐨𝐥𝐯𝐞𝐝 𝐢𝐧 𝐐𝟐. Not through a new retention program. Not through exit interviews. But through people analytics. One of our enterprise clients noticed an unexpected spike in high-performer exits—specifically in two product teams. Instead of guessing, their HRBP used early warning signals from internal mobility, engagement dips, and compensation mismatches. They uncovered a pattern: Mid-level engineers weren’t leaving for money—they were leaving for 𝐠𝐫𝐨𝐰𝐭𝐡. And this need an fix → A rapid redesign of career pathing and peer mentorship across engineering. Three months later: → Voluntary attrition dropped by 𝟑𝟔% → Internal mobility rose by 𝟐𝟐% → Estimated cost avoidance? $𝟓𝐌+ This isn’t a one-off. According to Deloitte, companies using advanced people analytics are 𝐭𝐰𝐢𝐜𝐞 𝐚𝐬 𝐥𝐢𝐤𝐞𝐥𝐲 𝐭𝐨 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐥𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 and 𝐭𝐡𝐫𝐞𝐞 𝐭𝐢𝐦𝐞𝐬 𝐦𝐨𝐫𝐞 𝐥𝐢𝐤𝐞𝐥𝐲 𝐭𝐨 𝐨𝐮𝐭𝐩𝐞𝐫𝐟𝐨𝐫𝐦 𝐩𝐞𝐞𝐫𝐬 𝐟𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥𝐥𝐲. But what really matters: 𝐘𝐨𝐮 𝐜𝐚𝐧’𝐭 𝐟𝐢𝐱 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐜𝐚𝐧’𝐭 𝐬𝐞𝐞. And too many leaders are still leading blind. Data isn’t just about efficiency. It’s about 𝐩𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐢𝐧 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐦𝐚𝐤𝐢𝐧𝐠—especially when your people are your biggest investment. #CHRO #HR #Dataanalytics #Datainsights #LeadershipPipelines

What’s driving retention and fueling attrition? 📊 Worklytics research reveals the key work behaviors that increase attrition risk and those that boost retention. The data is clear: burnout, weak networks, and poor manager support push employees out, while career growth, focus time, and leadership visibility keep them engaged. Let’s break it down. 👇 🚨 Attrition Risks: 🔸 Workdays over 9.5 hours → +15% attrition risk Burnout leads to disengagement and turnover. 🔸 Fewer than 1 manager 1:1 per month → +15% disengagement risk Lack of manager connection is a top predictor of quiet quitting. 🔸 Less than 3 hours of focus time per day → +12% burnout risk Too many meetings = stress, longer hours, and lower performance. 🔸 Weak networks (fewer than 4 strong collaborators) → +12% isolation risk Disconnected employees are far more likely to leave. 🔸 Overextended (17+ close collaborators) → +8% higher stress Too many work connections create cognitive overload. 🔸 More than 3 hours of weekend work → +8% higher turnover risk Lack of work-life boundaries accelerates burnout. 🔸 More than 2 manager changes in a year → +6% higher attrition Frequent leadership changes erode trust and slow career growth. ✅ Retention Drivers: ✔️ Workdays under 9 hours → 8% lower attrition risk Sustainable workloads improve job satisfaction and reduce burnout. ✔️ At least 2 manager 1:1s per month → 11% retention boost Regular check-ins strengthen engagement and career growth. ✔️ More than 4 hours of focus time per day → 8% better engagement Uninterrupted work time fuels productivity and satisfaction. ✔️ 6+ strong collaborators → 8% lower turnover risk Employees with strong internal networks are more committed. ✔️ Fewer than 23 after-hours Slack messages per week → 6% retention boost Healthy boundaries protect work-life balance. ✔️ 1 promotion in 18 months → 3% higher retention Clear career paths drive long-term engagement. ✔️ 1 team change in 24 months → 3% stronger internal mobility Internal moves prevent stagnation and boost development. Tracking these signals helps predict retention trends, identify at-risk employees, and design targeted interventions before it’s too late. See the comments below for the full analysis from our team at Worklytics. Which retention signals are you tracking? #PeopleAnalytics #HRAnalytics #WorkforceAnalytics #TalentAnalytics #FutureOfWork

When it comes to tackling employee attrition, the first step is to ground decisions in data. The Work From Anywhere partnership with Mercer breaks this down. In the Global Talent Trends survey, Mercado Libre used AI to analyze their entire benefits stack, pinpointing gaps based on what benefits employees were actually requesting. Mercado Libre: *Identified a benefits gap among software engineers. *Using AI insights, they launched a targeted “work-from-anywhere” program for that specific segment. *Within about a year, voluntary attrition dropped from 14 percent to 4 percent. 74% of those engineers now plan to stay with the company for more than three years. That’s a dramatic shift, but it’s not unique to Mercado Libre. An example is Spotify. After rolling out their own data-driven Work From Anywhere policy, they saw attrition fall by 15 percent in just one year. What these case studies show isn’t just about perks or programs. It’s about using AI to uncover where your people feel underserved, and then designing benefits that directly address those needs. To quote John Lee, CEO of Work From Anywhere, "The biggest saving isn’t just in dollars or time. It’s in retaining the talent you already have." If you’re still designing benefits based on hunches or outdated benchmarks, here are 3 questions John recommends to get you started... 1) How are you currently collecting and analyzing employee feedback on benefits? 2) Have you segmented your data by role, department or tenure to spot hidden gaps? 3) What tools, including AI or people-analytics platforms, could help you forecast the impact of a new program? I’d love to hear: how is your organization using data to reshape benefits and drive retention? Share your experiences below, and add WFA below for a link to the Global Talents Trends Survey. More clips from the latest episode of Progressive HR with guest John Lee coming soon.